1. Field of the Disclosure
This application relates generally to optical coherence tomography (OCT) imaging methods and image processing and, more specifically, to an OCT imaging method and processing utilizing volume rendering, and analysis and display of information of the retinal vasculature that utilizes the spatial position of the vessels.
2. Description of Related Art
Optical Coherence Tomography (OCT) is an optical imaging technique that uses interferometry of short-coherence light and processing techniques to capture cross-sectional information from biologic structures with micrometer resolution. (References 1-8 herein incorporated by reference in their entirety.) OCT imaging has been commonly used for non-invasive imaging of objects of interest, such as retina of the human eye.
In OCT imaging, successive one-dimensional scans looking into the tissue (“A-scans”) are aggregated into planar scans (“B-scans”) and in turn many sequential B-scans can be assembled into a three-dimensional block called a volume scan.
Flow information can be derived from OCT images by looking at phase information or by examining the change over time of phase or amplitude data. In comparing the reflectance images of more than one scan the amount of decorrelation among the images can be calculated on a voxel by voxel basis. Stationary tissue produces reflections having little decorrelation while moving tissue has high decorrelation. Assuming no bulk motion of the patient, motion in the retina is caused by blood flow in retinal vessels. Thus areas of decorrelation of the low-coherence originating signal is indicative of blood flow in vessels running through the examined thickness of the retina. (References 9-17 herein incorporated by reference in their entirety.)
The typical way to display flow information in such a volume of tissue has been to create a virtual 2D image by projecting lines through the volume and selecting the maximal decorrelation value present for inclusion onto the virtual 2D image. This method is called the maximal intensity projection. The resulting 2D image that can be readily evaluated on a computer monitor. By choosing the maximum intensity and collapsing this onto a 2D representation, maximal intensity projection can lose 3D data that is very significant to analyzing the health or disease state of the tissue. An alternate strategy of using the average voxel value suffers the same loss of depth information.
To aid in evaluating the tissue and blood vessels therein the OCT data can be split into anatomic layers through a process called segmentation. The retina is a curved structure, and the flat planes initially produced in OCT slice across many layers, making interpretation difficult. Since retinal layers in a healthy eye have a characteristic and differentiable appearance, it is possible to select boundaries of a layer through a process of segmentation, extending across the lateral topography of the retina. However, since such segmentation is based on structural assumptions relating to healthy tissue. Segmentation algorithms developed for healthy tissue do not work properly for diseased tissue, which may be abnormal in many different ways that are difficult to anticipate in a segmentation algorithm. While segmentation can be useful in a healthy eye, segmentation errors can obscure important disease-related information.
The present disclosure addresses methods that take a different approach to analyzing and visualizing OCT data, seeking instead to visualize vessels through the depth of tissue and to evaluate vascular size, shape, connectivity, and density based on this different type of visualization.